Published on November 7, 2023, 2:14 pm

TLDR: Generative AI has the potential for significant productivity gains but can occasionally present incorrect information, known as "hallucinations." IT organizations can minimize this risk by implementing techniques such as retrieval-augmented generation (RAG), fine-tuning, prompt engineering, and user training and best practices. RAG allows models to retrieve information from relevant datasets, while fine-tuning involves retraining models with specific data. Prompt engineering helps train models to respond predictably, and user training ensures proper usage of large language models. These strategies should be tailored to each organization's use cases and resources, considering factors like security and customization. Seeking the support of partners like Dell can accelerate progress in generative AI implementation.

Generative AI is becoming increasingly popular in the workplace due to its potential for significant productivity gains. However, one of its main challenges is the occasional occurrence of “hallucinations,” where it presents incorrect information as factual. These hallucinations can be detrimental to organizations, leading to embarrassing situations and a loss of brand trust.

Fortunately, there are several actions that IT organizations can take to minimize the risk of generative AI hallucinations. They can either make decisions within their own environments or focus on training internal users on how to effectively use existing tools. Here are some options that IT teams can consider:

1. Retrieval-augmented generation (RAG): This technique allows models to retrieve information from specified datasets or knowledge bases. By using relevant documents as data sources, RAG enables more accurate outputs and can be implemented easily with readily available code snippets.

2. Fine-tuning: Unlike RAG, fine-tuning involves retraining a large language model with data to generate content more accurately based on that specific data. Combining fine-tuned models with RAG has shown significant reductions in hallucinations.

3. Prompt engineering: This process involves interactively training a large language model through a question-and-answer approach. By using specific prompt engineering techniques, models can be trained to respond in more predictable ways, thereby increasing problem-solving accuracy.

4. User training and best practices: It is crucial to ensure that users have adequate training in maximizing the potential of large language models and follow best practices such as peer reviews and fact-checking before publishing content. Teaching users how to create clear prompts with sufficient context and reviewing outputs with subject matter experts and peers can help reduce errors.

It’s important for organizations embarking on their generative AI journeys to address the risks associated with AI hallucinations by implementing these strategies. Organizations may choose to employ multiple approaches, combining model training or augmentation with user education for comprehensive coverage. It’s worth noting that these strategies are not exhaustive and their effectiveness will depend on the specific use cases and available resources of each organization. Factors like security and customization should also be considered when deciding on deployment options.

No matter where organizations are in their generative AI journey, following these steps can help mitigate the risks of hallucinations. Furthermore, seeking the support of partners, such as Dell, can accelerate progress by providing guidance, identifying use cases, implementing solutions, increasing adoption, and training internal users to drive innovation faster.

To learn more about generative AI and its potential benefits for your organization, visit


Comments are closed.